Identification of text regions in papers that are optically scanned (e.g. by a flatbed scanner of a photocopier) is significantly easier (e.g. due to upright orientation, large size and slow speed) than detecting regions that may contain text in scenes of the real world that may be captured in images (also called “natural images”) or in video frames in real time by a handheld device (such as a smartphone) having a built-in digital camera. Specifically, optical character recognition (OCR) methods of the prior art originate in the field of document processing, wherein the document image contains a series of lines of text (e.g. 30 lines of text) of an optically scanned page in a document. Document processing techniques, although successfully used on scanned documents created by optical scanners, generate too many false positives and/or negatives so as to be impractical when used on natural images containing text in various fonts e.g. on traffic signs, store fronts, vehicle license plates, due to variations in lighting, color, tilt, focus, font, etc.
FIG. 1 illustrates a bill board in the real world scene 100 in India. A user 110 (see FIG. 1) may use a camera-equipped mobile device (such as a cellular phone) 108 to capture an image 107 (also called “natural image” or “real world image”) of scene 100. Camera captured image 107 may be displayed on a screen 106 of mobile device 108. Such an image 107 (FIG. 1), if processed directly using prior art image processing techniques may result in failure to recognize one or more words in a region 103 (FIG. 1). However, use of prior art methods can cause problems when the image quality is poor for one or more reasons noted above e.g. due to variations in lighting, color, tilt, focus, font, etc.
Accordingly, there is a need to improve image quality prior to identification of characters in blocks of a region of text in a natural image or video frame, as described below.